The present invention relates generally to a text classification, ranking, and forecasting based on sentimentality. More particularly, the present invention relates to a system, method and software program product for the sentiment ranking of documents based on movement in a related activity.
In today's business climate, the availability of timely information is essential to a successful operation. This information comes in many forms, such as daily, weekly, monthly and even quarterly publications that are available from a multitude of different authors and publishers, but can usually be acquired from an online media source. With this overabundance of seemingly relevant news, many businesspeople find themselves inundated with a plethora of information to examine on a daily basis. Because of these time constraints, only a small percentage of all news that is available can be examined comprehensively. The difficulty is in selecting only the most relevant articles and excluding less relevant news.
Most businesspeople select only the articles relevant to the subject matter that are important to them at the time. However, subject matter filters, such as keyword searches and the like, rarely ever reduce the amount of information to a manageable amount. Therefore, the relevant news articles are often further filtered by familiarity to the user, that is, by authors, publishers and media sources that the businessperson are familiar with. This mythology often results in a tunnel vision to all news articles except those having some familiarity to the businessperson. Occasionally, an article from an unfamiliar source may be selected for examination that is suggested by a friend or respected colleague, but by in large, time constraints limit a businessperson to familiar publications.
In some businesses, such as equity trading, the number of news articles returned from familiar sources may be quite extensive, perhaps ten to fifty articles per day. Considering that most traders track tens to hundreds of unique equities, indices and instruments, the actual number of news articles to be examined each day may be in the hundreds or even thousands. In addition, since each of the news articles returned by the keyword/familiarity filtering may be considered as equally important to every other article returned, it is difficult for a user to select only the most important articles to the exclusion of lesser important articles.
The combination of familiarity and keyword filtering narrowly limit the scope of document to be returned based on the businessperson's proficiency with document filters and experience with a particular topic. However, even if the businessperson is reasonably proficient with document searches and well experienced, the volume of news articles returned for review can be overwhelming. What is needed is another method of document filtering to return only a sample of the most relevant documents, regardless of the businessperson's familiarity with the publication.